The ease and low cost of electronic communications has resulted in an explosion of transmitted information. Individuals, particularly corporate users, are subject to an ever increasing volume of email. Email users, especially those with broad interest or job scope, may receive hundreds of emails daily. All of these emails must be sorted through in order to prioritize those communications that demand attention and eliminate those that have no value to the recipient. Additionally, emails need to be cataloged, categorized, or sorted so that they can be readily accessed at a later time. It is desirable to perform all of these tasks in an efficient manner.
Typical solutions for handling email include viewing inbound mail by priority; for example, by color coding inbox views based on the email sender. Email is often analyzed based on content and manually or automatically assigned tags, or attributes to better allow future reference. A user may often manually examine and pigeonhole email, assigning tags, or filing the email in named folders. Storing email can also be done by algorithm based on time, source, topic. Machine learning algorithms can study an email user's patterns and recommend information storage schemes, or inbound attention priority schemes. These suffer from various problems, for example, not all mail from a source may have the same connotations of urgency, topic, or importance. Manual methods for handling email are slow and effortful. While faster, and requiring less effort on the part of the user, automated analysis may fail when email correspondents are uninformed or overdramatic (e.g., when the email is written to dramatize a situation which is not dramatic, or encourage action which is unnecessary). Additionally, machine learning can reinforce poor patterns of information management, learning from the email user's errors as well as her successes. Furthermore, as users collaborate with their colleagues, it is often discovered that initial sorting, or attribute tagging may be wrong, for example, as the user comes to better understands an evolving situation.
As a result of increasing email volume and slow and ineffective methods for handling received email, accessing email once received can also be difficult. The sheer volume of email can overwhelm even cleverly conceived storage pattern. Similarly, with high volumes of information even well crafted email search engines can provide too many responses, as well as being unreflective of the context in which the mail was received.